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---
title: BOBYQA (v0.31)
description: API reference for qiskit.algorithms.optimizers.BOBYQA in qiskit v0.31
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.algorithms.optimizers.BOBYQA
---
# BOBYQA
<Class id="qiskit.algorithms.optimizers.BOBYQA" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.18/qiskit/algorithms/optimizers/bobyqa.py" signature="BOBYQA(maxiter=1000)" modifiers="class">
Bases: `qiskit.algorithms.optimizers.optimizer.Optimizer`
Bound Optimization BY Quadratic Approximation algorithm.
BOBYQA finds local solutions to nonlinear, non-convex minimization problems with optional bound constraints, without requirement of derivatives of the objective function.
Uses skquant.opt installed with pip install scikit-quant. For further detail, please refer to [https://github.com/scikit-quant/scikit-quant](https://github.com/scikit-quant/scikit-quant) and [https://qat4chem.lbl.gov/software](https://qat4chem.lbl.gov/software).
**Parameters**
**maxiter** (`int`) Maximum number of function evaluations.
**Raises**
[**MissingOptionalLibraryError**](qiskit.aqua.MissingOptionalLibraryError "qiskit.aqua.MissingOptionalLibraryError") scikit-quant not installed
## Methods
<span id="qiskit-algorithms-optimizers-bobyqa-get-support-level" />
### get\_support\_level
<Function id="qiskit.algorithms.optimizers.BOBYQA.get_support_level" signature="BOBYQA.get_support_level()">
Returns support level dictionary.
</Function>
<span id="qiskit-algorithms-optimizers-bobyqa-gradient-num-diff" />
### gradient\_num\_diff
<Function id="qiskit.algorithms.optimizers.BOBYQA.gradient_num_diff" signature="BOBYQA.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)" modifiers="static">
We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center.
**Parameters**
* **x\_center** (*ndarray*) point around which we compute the gradient
* **f** (*func*) the function of which the gradient is to be computed.
* **epsilon** (*float*) the epsilon used in the numeric differentiation.
* **max\_evals\_grouped** (*int*) max evals grouped
**Returns**
the gradient computed
**Return type**
grad
</Function>
<span id="qiskit-algorithms-optimizers-bobyqa-optimize" />
### optimize
<Function id="qiskit.algorithms.optimizers.BOBYQA.optimize" signature="BOBYQA.optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)">
Runs the optimization.
</Function>
<span id="qiskit-algorithms-optimizers-bobyqa-print-options" />
### print\_options
<Function id="qiskit.algorithms.optimizers.BOBYQA.print_options" signature="BOBYQA.print_options()">
Print algorithm-specific options.
</Function>
<span id="qiskit-algorithms-optimizers-bobyqa-set-max-evals-grouped" />
### set\_max\_evals\_grouped
<Function id="qiskit.algorithms.optimizers.BOBYQA.set_max_evals_grouped" signature="BOBYQA.set_max_evals_grouped(limit)">
Set max evals grouped
</Function>
<span id="qiskit-algorithms-optimizers-bobyqa-set-options" />
### set\_options
<Function id="qiskit.algorithms.optimizers.BOBYQA.set_options" signature="BOBYQA.set_options(**kwargs)">
Sets or updates values in the options dictionary.
The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.
**Parameters**
**kwargs** (*dict*) options, given as name=value.
</Function>
<span id="qiskit-algorithms-optimizers-bobyqa-wrap-function" />
### wrap\_function
<Function id="qiskit.algorithms.optimizers.BOBYQA.wrap_function" signature="BOBYQA.wrap_function(function, args)" modifiers="static">
Wrap the function to implicitly inject the args at the call of the function.
**Parameters**
* **function** (*func*) the target function
* **args** (*tuple*) the args to be injected
**Returns**
wrapper
**Return type**
function\_wrapper
</Function>
## Attributes
### bounds\_support\_level
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.bounds_support_level">
Returns bounds support level
</Attribute>
### gradient\_support\_level
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.gradient_support_level">
Returns gradient support level
</Attribute>
### initial\_point\_support\_level
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.initial_point_support_level">
Returns initial point support level
</Attribute>
### is\_bounds\_ignored
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_bounds_ignored">
Returns is bounds ignored
</Attribute>
### is\_bounds\_required
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_bounds_required">
Returns is bounds required
</Attribute>
### is\_bounds\_supported
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_bounds_supported">
Returns is bounds supported
</Attribute>
### is\_gradient\_ignored
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_gradient_ignored">
Returns is gradient ignored
</Attribute>
### is\_gradient\_required
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_gradient_required">
Returns is gradient required
</Attribute>
### is\_gradient\_supported
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_gradient_supported">
Returns is gradient supported
</Attribute>
### is\_initial\_point\_ignored
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_initial_point_ignored">
Returns is initial point ignored
</Attribute>
### is\_initial\_point\_required
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_initial_point_required">
Returns is initial point required
</Attribute>
### is\_initial\_point\_supported
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.is_initial_point_supported">
Returns is initial point supported
</Attribute>
### setting
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.setting">
Return setting
</Attribute>
### settings
<Attribute id="qiskit.algorithms.optimizers.BOBYQA.settings">
**Return type**
`Dict`\[`str`, `Any`]
</Attribute>
</Class>